and \(L^f = \lbrace l^f_1, \cdots , l^f_{M}\rbrace ,~l^f_i \in [0, (HW/(32\times 32))-1]\)  is the prediction from the grid classification.
We can effectively solve for the unknown pose \(\hat{G} \in \text{SE}(3)\)  in the PnP problem after
resizing the image into \(1/32\)  of the original size.
Accordingly, the camera intrinsics \(K^{\prime }\)  after the resize is obtained by dividing \(f_x\) , \(f_y\) , \(c_x\) , \(c_y\)  with 32.
There are many off-the-shelf PnP solver like EPnP [1]}, UPnP [2]}, etc. We apply RANSAC on EPnP provided by OpenCV to robustly solve for \(\hat{G}\) .
